PCA Lab

data <- read.csv("~/data.csv1.csv", row.names = 1)

chooseCRANmirror(ind = 1)
install.packages(c("factoextra", "FactoMineR"))

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## 
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library("factoextra")

## Warning: package 'factoextra' was built under R version 4.2.3

## Loading required package: ggplot2

## Warning: package 'ggplot2' was built under R version 4.2.3

## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

library("FactoMineR")

## Warning: package 'FactoMineR' was built under R version 4.2.3

pca.data <- PCA(data[,-1], scale.unit = TRUE, graph = FALSE)

fviz_eig(pca.data, addlabels = TRUE, ylim = c(0, 70))

fviz_pca_var(pca.data, col.var = "cos2",
             gradient.cols = c("#FFCC00", "#CC9933", "#660033", "#330033"),
             repel = TRUE) 

pca.data <- PCA(t(data[,-1]), scale.unit = TRUE, graph = FALSE)

fviz_pca_ind(pca.data, col.ind = "cos2", 
                  gradient.cols = c("#FFCC00", "#CC9933", "#660033", "#330033"), 
                  repel = TRUE)

install.packages("devtools")

## Installing package into 'C:/Users/ifthe/AppData/Local/R/win-library/4.2'
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devtools::install_github("kassambara/ggpubr")

## WARNING: Rtools is required to build R packages, but is not currently installed.
## 
## Please download and install Rtools 4.2 from https://cran.r-project.org/bin/windows/Rtools/.

## Skipping install of 'ggpubr' from a github remote, the SHA1 (6aeb4f70) has not changed since last install.
##   Use `force = TRUE` to force installation

library(ggpubr)

a <- fviz_pca_ind(pca.data, col.ind = "cos2", 
                  gradient.cols = c("#FFCC00", "#CC9933", "#660033", "#330033"), 
                  repel = TRUE)

ggpar(a,
      title = "Principal Component Analysis",
      xlab = "PC1", ylab = "PC2",
      legend.title = "Cos2", legend.position = "top",
      ggtheme = theme_minimal())

pca.data <- PCA(data[,-1], scale.unit = TRUE,ncp = 2, graph = FALSE)

data$Lineage <- as.factor(data$Lineage)

install.packages("RColorBrewer")

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library(RColorBrewer)
nb.cols <- 3
mycolors <- colorRampPalette(brewer.pal(3, "Set1"))(nb.cols)

a <- fviz_pca_ind(pca.data, col.ind = data$Lineage,
                  palette = mycolors, addEllipses = TRUE)

ggpar(a,
      title = "Principal Component Analysis",
      xlab = "PC1", ylab = "PC2",
      legend.title = "Cell type", legend.position = "top",
      ggtheme = theme_minimal())